{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generative-adversarial-active-learning-for","title":"Generative Adversarial Active Learning for Unsupervised Outlier Detection","arxiv_id":"1809.10816","date":"2018-09-28","proceeding":null,"authors":["Yezheng Liu","Zhe Li","Chong Zhou","Yuanchun Jiang","Jianshan Sun","Meng Wang","Xiangnan He"],"abstract":"Outlier detection is an important topic in machine learning and has been used\nin a wide range of applications. In this paper, we approach outlier detection\nas a binary-classification issue by sampling potential outliers from a uniform\nreference distribution. However, due to the sparsity of data in\nhigh-dimensional space, a limited number of potential outliers may fail to\nprovide sufficient information to assist the classifier in describing a\nboundary that can separate outliers from normal data effectively. To address\nthis, we propose a novel Single-Objective Generative Adversarial Active\nLearning (SO-GAAL) method for outlier detection, which can directly generate\ninformative potential outliers based on the mini-max game between a generator\nand a discriminator. Moreover, to prevent the generator from falling into the\nmode collapsing problem, the stop node of training should be determined when\nSO-GAAL is able to provide sufficient information. But without any prior\ninformation, it is extremely difficult for SO-GAAL. Therefore, we expand the\nnetwork structure of SO-GAAL from a single generator to multiple generators\nwith different objectives (MO-GAAL), which can generate a reasonable reference\ndistribution for the whole dataset. We empirically compare the proposed\napproach with several state-of-the-art outlier detection methods on both\nsynthetic and real-world datasets. The results show that MO-GAAL outperforms\nits competitors in the majority of cases, especially for datasets with various\ncluster types or high irrelevant variable ratio.","url_abs":"http://arxiv.org/abs/1809.10816v4","url_pdf":"http://arxiv.org/pdf/1809.10816v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generative-adversarial-active-learning-for","repo_url":"https://github.com/leibinghe/GAAL-based-outlier-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-active-learning-for","repo_url":"https://github.com/qarchli/pytorch-gan-for-outlier-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.10816","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.10816"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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